Neuromorphic computing systems : crystalline resistive random access memory

Neuromorphic computing is a promising approach for efficient electronics by shaping computer hardware like the human brain. At the core of neuromorphic architectures are artificial synapses, which store conductance states to weight collections of electrical spikes according to Kirchoff's laws and Ohm's law. This thesis presents Silicon (Si)-based crystalline resistive random-access memory (crystalline RRAM) artificial synapses for neuromorphic computing. The main scaling bottleneck is poor temporal and spatial uniformity of artificial synapses. To the best of the author's knowledge, crystalline RRAM reported in this thesis have the lowest switching variations compared to other RRAM types. Controlling metal movement in resistive switching materials is extremely challenging. This thesis demonstrates two strategies to improve nanoscale control in crystalline RRAM: 1) intrinsic semiconductor regulation and 2) active metal alloying.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Tan, Scott H.(Scott Howard)

Advisor dc:contributor.advisor

• Jeehwan Kim.

• Mechanical Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127915

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127915